Iterative adaptive solar tracking having variable step size

ABSTRACT

A system controller for position controlling a photovoltaic (PV) panel in a PV system including a power sensor sensing output power (P), and a motor for positioning the PV panel. The system controller includes a computing device having memory that provides motor control signals and implements an iterative adaptive control (IAC) algorithm stored in the memory for adjusting an angle of the PV panel. The IAC algorithm includes an iterative relation that relates P at current time k (P(k)), its elevation angle at k (θ s  (k)), P after a next step (P(k+1)) and its elevation angle at k+1 (θ s  (k+1)). The IAC algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates a position angle θ S  (k+1) of the PV panel using the perturbed power value. The motor control signals from the computing device cause the motor to position the PV panel to achieve θ S  (k+1).

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Provisional Application Ser. No.61/376,480 entitled “ITERATIVE ADAPTIVE SOLAR TRACKING HAVING VARIABLESTEP SIZE”, filed Aug. 24, 2010, which is herein incorporated byreference in its entirety.

TECHNICAL FIELD

Disclosed embodiments relate to photovoltaics, and more specifically tophotovoltaic systems having iterative solar tracking.

BACKGROUND

In solar technology, the photovoltaic (PV) panel orientation relative tothe sun's rays determines power production. On a clear day, power ismaximized when the solar panel is oriented normal to the direction sun'srays. During days with dubious weather, the actual maximum point ofpower production may not be exclusively based on the position of the sundue to atmospheric scattering. Also, because the sun's position in thesky moves throughout the day, the elevation angle for a solar panelneeds to be changed several times each hour (e.g., every minute) toprovide maximum output power. Over longer periods of time, the azimuthposition for maximum power output changes as well, requiring theazimuthal angle of the solar panel to also be changed.

Sun tracking allows a PV panel to follow motion of the sun to helpmaximize power. Sun tracking and can be performed manually, orautomatically. Conventional automation requires sensors to determine thelocation of the sun, or to have a pre-programmed tracking path topredict the position of the sun to obtain in an attempt to obtainmaximum power throughout the day. These systems can be expensive andinaccurate. There is thus a need to create a more efficient system andmethod for automatic solar tracking.

SUMMARY

Disclosed embodiments describe iterative adaptive solar tracking thatuses a variable step size for adjusting the angle of photovoltaic (PV)panel(s) which may be contrasted with known iterative solar trackingthat uses a constant step size for angular adjustments of the PV panel.A PV system including solar tracking comprises at least one PV panel forreceiving radiation, a power sensor for detecting output power (P) fromthe PV panel, and a system controller comprising at least one computingdevice (e.g., microprocessor or microcontroller) that provides motorcontrol signals and implements a disclosed iterative adaptive control(IAC) algorithm.

The IAC algorithm includes an iterative relation that increases theoutput power P from the PV panel by iteratively adjusting its elevationangle θ_(S), and optionally also the azimuthal angle θ_(AZ), to trackthe position of the sun. The output power P is used as the performancefunction in the IAC algorithm which is maximized using an adaptivegradient ascent approach.

The IAC algorithm is based on the Inventors recognizing that the outputpower P depends on the maximum direct-beam solar radiation intensityH_(n) and the angle between the normal to the surface of the PV paneland the sun's rays θ₁. Since H_(n) varies with the time of day and year,and thus cannot be controlled, the IAC algorithm maximizes the outputpower P by minimizing θ₁. The PV system perturbs the output power P, andthe perturbed power value is used in the iterative relation toadaptively estimate the corresponding position angle θ_(S) (andoptionally also θ_(AZ)) for the PV panel(s) to achieve this perturbedpower value. The resulting P is then measured. The iterative process cancontinue until there is no appreciable change in P.

Furthermore, at every iteration of the iterative relation, non-constantstep sizes can be generated to update the position angles. This allowssuperior convergence properties for the IAC algorithm in terms ofconvergence speed, accuracy, and stability in contrast to known suntracking methods that employ fixed step size angular position updates.

Also disclosed is a system controller for position controlling a PVpanel in a PV system including a power sensor sensing output power (P),and a motor for positioning the PV panel. The system controller includesa computing device having memory that provides motor control signals andimplements an IAC algorithm stored in the memory for adjusting an angleof the PV panel. The IAC algorithm includes an iterative relation thatrelates P at current time k (P(k)), its elevation angle at k (θ_(s)(k)), P after a next step (P(k+1)) and its elevation angle at k+1 (θ_(s)(k+1)). The IAC algorithm generates a perturbed power value P(k+1) toprovide a power perturbation to P(k), and calculates a position angleθ_(S) (k+1) of the PV panel using the perturbed power value. The motorcontrol signals from the computing device cause the motor to positionthe PV panel to achieve θ_(S) (k+1).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing steps in an exemplary method of automaticsolar tracking using a variable step size for adjusting the angle of aPV panel, according to a disclosed embodiment.

FIG. 2 is a diagram showing general geometric relationships between thesun and a planar PV panel.

FIG. 3 is a block diagram representation of an example PV systemincluding a disclosed system controller that implements a disclosed suntracking algorithm, according to a disclosed embodiment.

FIG. 4 is simulation data showing tracking error vs. solar elevationangle, according to a disclosed embodiment.

FIG. 5 is simulation data showing output power vs. solar elevationangle, according to a disclosed embodiment.

FIG. 6 is simulation data showing the number of iterations vs. solarelevation angle, according to a disclosed embodiment.

DETAILED DESCRIPTION

Disclosed embodiments in this Disclosure are described with reference tothe attached figures, wherein like reference numerals are usedthroughout the figures to designate similar or equivalent elements. Thefigures are not drawn to scale and they are provided merely toillustrate the disclosed embodiments. Several aspects are describedbelow with reference to example applications for illustration. It shouldbe understood that numerous specific details, relationships, and methodsare set forth to provide a full understanding of the disclosedembodiments. One having ordinary skill in the relevant art, however,will readily recognize that the subject matter disclosed herein can bepracticed without one or more of the specific details or with othermethods. In other instances, well-known structures or operations are notshown in detail to avoid obscuring structures or operations that are notwell-known. This Disclosure is not limited by the illustrated orderingof acts or events, as some acts may occur in different orders and/orconcurrently with other acts or events. Furthermore, not all illustratedacts or events are required to implement a methodology in accordancewith this Disclosure.

Notwithstanding that the numerical ranges and parameters setting forththe broad scope of this Disclosure are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspossible. Any numerical value, however, inherently contains certainerrors necessarily resulting from the standard deviation found in theirrespective testing measurements. Moreover, all ranges disclosed hereinare to be understood to encompass any and all sub-ranges subsumedtherein. For example, a range of “less than 10” can include any and allsub-ranges between (and including) the minimum value of zero and themaximum value of 10, that is, any and all sub-ranges having a minimumvalue of equal to or greater than zero and a maximum value of equal toor less than 10, e.g., 1 to 5.

FIG. 1 is a flow chart showing steps in an example method 100 ofautomatic solar tracking using a variable step size for adjusting theangle of at least one PV panel, according to a disclosed embodiment.Step 101 shown as start comprises providing at least one PV panel havinga panel surface for receiving solar radiation, a power sensor coupled toan output of the PV panel for sensing an output power (P) provided bythe PV panel, and a system controller. The system controller includes atleast one computing device (e.g., microprocessor) having an associatedmemory that provides motor control signals and implements an IACalgorithm stored in the memory for adjusting the angle of the PV panel.The IAC algorithm includes an iterative relation that relates the P at acurrent time k (P(k)), its elevation angle at k (θ_(S) (k)), P after anext step (P(k+1)) and its elevation angle at time k+1 (θ_(s) (k+1)).The IAC algorithm can also adjust θ_(AZ) Step 102 comprises sensing thepower output of the PV panel. Step 103 comprises calculating a change inP and determining whether P changed significantly (e.g., relative to apredetermined P change limit). Step 104 comprises waiting apredetermined waiting period (e.g., 1 to 30 minutes as shown in FIG. 1)before returning to step 102 for another power sensor reading.Accordingly, at any point in the day, the method will wait for thepredetermined waiting period between power sensor readings, and if thereis no significant change in power (step 103), the method will continueto wait another predetermined waiting period (e.g., 1 to 30 minutes).The predetermined waiting period will not measurably affect theperformance of the system controlled by method 100 as the earth movesslowly around the sun.

During startup, the registers or other memory device associated with thepower sensor that stores sensed P values for step 102 are generallyinitialized to 0. Hence, at startup there will be no change in P at step103 and step 104 will redirect the method 100 at startup after thepredetermined waiting period (e.g., 1 to 30 minutes as shown in FIG. 1)for another P reading. As a result, there will not be an initial powerperturbation (step 106, described below) at startup until the powersensor in step 102 senses a non zero value of P. During daylight thesensed P is always positive, and when the sensed P is determined to beappreciable (step 103), the method will find an increase in P (step 105)and the method will reach step 106 (power perturbation).

During operation of method 100 following startup, the change in Pdetermined in step 103 will be the change in P resulting from theupdated PV panel position resulting from the positioning step describedbelow (step 108). As with startup, if the change in P is below apre-determined P change limit, step 104 is triggered which compriseswaiting a predetermined waiting period then returning to step 102 foranother power sensor reading. However, if the change in power at step103 is at or above the pre-determined limit that defines an appreciableP change, step 105 is triggered which determines whether the P hasincreased or decreased to direct step 106 to perturb the power value foruse in the iterative relation in the proper direction.

Step 106 comprises generating a perturbed power value P(k+1) to providea power perturbation to the current P(k) for use in the iterativerelation. The power can be perturbed in the positive or negativedirection. Perturbing the power in the positive direction meansincreasing the power to be achieved, not the angle. As noted above, theangle is what is estimated by the IAC corresponding to the perturbedpower. Likewise, perturbing power in the negative direction meansdecreasing the power to be achieved. If the output power P isincreasing, the power perturbations continue to add a positive term tothe current output power measurement (sensed at step 102), and if theoutput power P has decreased, it is known the movement should be in theopposite direction. Therefore, a term is subtracted from the currentoutput power measurement to provide a reduced perturbed power value.

Once step 106 generates a perturbed power value to be achieved, themethod moves to step 107 to find the corresponding position angle toachieve this perturbed power value. Step 107 comprises calculating aposition angle θ_(S) (k+1) using the value for perturbed power value inthe iterative relation. As described below, the iterative relation usesa variable step size for adjusting θ_(S) that is based on both P(k+1)and P(k).

Step 108 comprises positioning the PV panel to achieve θ_(S) (k+1). Thepositioning can comprise sending a suitable control signal to a steppermotor to update the position of the PV panel to achieve θ_(S) (k+1). Ifthe motor is a stepper motor, the control signal will be a pulse widthmodulation (PWM) signal. However, if a different type of motor is used,other signal modes can be used.

The method can optionally also apply a related IAC algorithm to also suntrack the azimuthal position angle θ_(AZ) simultaneously with theelevation angle θ_(S). It is noted that θ_(AZ) is known to notmeasurably change unless the change is measured in weeks.

FIG. 2 is a diagram showing general geometric relationships and relevantparameters between the sun and a planar PV panel for an embodiment thatiteratively positions the θ_(S) for a solar panel, that is used inreference to the exemplary sun tracking algorithm described below. H isthe power/area and H_(b) is the incident direct beam (maximum; beingnormal to the surface of the PV panel) solar radiation.

FIG. 3 is a block diagram representation of an example PV system 300having a system controller that implements a disclosed sun trackingalgorithm, according to a disclosed embodiment. System 300 includes atleast one PV panel 310 comprising an interconnected assemblyphotovoltaic cells having a panel surface 311 for receiving solarradiation and convert the solar radiation to electrical power. Because asingle PV panel 310 can produce only a limited amount of power, in atypical application there will be several PV panels.

A power sensor 315 is coupled to an output of the PV panel 310 forsensing the output power (P) provided by the PV panel. System 300includes a system controller 320 including at least one computing device321 (e.g., processor) having associated memory 322 and motor drivingcircuitry 323 that provides motor control signals 327. System controller320 implements an iterative adaptive control (IAC) algorithm that isstored in the memory 322 for adjusting an angle of the PV panel 310. TheIAC algorithm includes an iterative relation that relates P at a currenttime k (P(k)), its elevation angle at k (θs (k)), P after a next step(P(k+1)) and its elevation angle at k+1 (θs (k+1)), wherein the IACalgorithm is operable to generate a perturbed power value P(k+1) toprovide a power perturbation to P(k), and calculate a position angle(k+1) of the PV panel 310 using the perturbed power value in theiterative relation.

The system 300 also includes a mechanical system comprising a motor(e.g., a DC motor) 330 coupled to receive the motor control signals 327provided by motor driving circuitry 323. The motor control signals areconfigured for positioning the PV panel to achieve θs (k+1). Althoughshown in FIG. 3 controlling a single motor 330, motor driving circuitry323 can control a plurality of motors to allow control of the angularposition of a plurality of PV panels.

Mathematical Description of an Exemplary Algorithm

One approach for iteratively adjusting θ_(S) for solar panels isdescribed below in equation form as follows. Firstly, the followingparameters are defined—k: iteration index, μ(k): step size of positionangle increment/decrement at k, α: elevation angle of the sun which isan unknown.

The update equation for the position angle is given as:

$\begin{matrix}{{\theta_{s}\left( {k + 1} \right)} = {{\theta_{s}(k)} + {{\mu (k)}\frac{\partial{P(k)}}{\partial{\theta_{s}(k)}}}}} & (1)\end{matrix}$

The output power P of the PV panel is directly proportional to theincident direct beam solar radiation (H_(b)) given by the followingrelation:

P=H _(b) *A  (2)

where, A is the area of the PV panel.

Substituting (2) in (1), the following is obtained:

$\begin{matrix}{{\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} + {{\mu (k)}\frac{{\partial H_{b}}*A}{\partial{\theta_{S}(k)}}}}} & (3)\end{matrix}$

H_(b) can be expressed as

H _(b) =H _(n) cos(θ₁)  (4)

where, H_(n) is the maximum direct-beam solar radiation intensity, andθ₁ is the angle between the normal to the PV panel surface and the sun'srays. As mentioned above, since no sensors are needed to measure theradiation received from the sun, H_(n) is estimated adaptively. Hence,substituting (4) in (3), the following is obtained:

$\begin{matrix}{{\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} + {{\mu (k)}\frac{{\partial H_{n}}{\cos \left( \theta_{I} \right)}*A}{\partial{\theta_{S}(k)}}}}} & (5)\end{matrix}$

However, from FIG. 2, it can be seen that at any point in time thefollowing relation exists:

θ₁+α+θ_(S)=90°  (6)

When the PV panel is tracking the sun, θ₁=0, and correspondingly,α+θ_(S)=90°.

Hence, substituting for θ₁ in (5), the following is obtained:

$\begin{matrix}{{\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} + {{\mu (k)}\frac{{\partial H_{n}}{\cos \left( {90 - \left\{ {\alpha + \theta_{S}} \right\}} \right)}*A}{\partial{\theta_{S}(k)}}}}} & (7)\end{matrix}$

Combining θ_(S) and α into one angle θ_(γ), i.e. θ_(S)+α=θ_(γ),rewriting (1) as an optimization expression with respect to θ_(γ) asfollows

$\begin{matrix}{{\theta_{\gamma}\left( {k + 1} \right)} = {{\theta_{\gamma}(k)} + {{\mu (k)}\frac{\partial{P(k)}}{\partial{\theta_{\gamma}(k)}}}}} & (8)\end{matrix}$

Therefore, (8) can be rewritten as

$\begin{matrix}{{\theta_{\gamma}\left( {k + 1} \right)} = {{\theta_{\gamma}(k)} + {{\mu (k)}\frac{{\partial H_{n}}{\sin \left( \theta_{\gamma} \right)}*A}{\partial{\theta_{\gamma}(k)}}}}} & (9)\end{matrix}$

Equations (10)-(13) described below provide a calculation of the stepsize. By evaluating (9) the following is obtained:

θ_(γ)(k+1)=θ_(γ)(k)+μ(k)H _(n) cos(θ_(γ))*A  (10)

Since the measured output power P is a function of θ_(γ), P can beexpanded in an expansion, such as in a second order Taylor's seriesexpansion with respect to θ_(γ) as follows:

$\begin{matrix}{{P\left( {k + 1} \right)} = {{P(k)} + {\frac{\partial{P(k)}}{\partial{\theta_{\gamma}(k)}}{{\Delta\theta}_{\gamma}(k)}} + {\frac{1}{2!}{\frac{\partial^{2}{P(k)}}{\partial{\theta_{\gamma}(k)}^{2}}\left\lbrack {{\Delta\theta}_{\gamma}(k)} \right\rbrack}^{2}}}} & (11)\end{matrix}$

It is possible to use a Taylor series of 3rd order, or higher than 3rdorder. However, raising the order beyond 2nd order does not generallyresult in any appreciable increase in accuracy. In addition, itsignificantly increases the computational complexity of the algorithm.From (10), the following is obtained

Δθ_(γ)(k)=μ(k)H _(n) cos (θ_(γ))*A  (12)

Substituting (12) in (11), and performing the following derivative,

$\begin{matrix}{\frac{\partial{P\left( {k + 1} \right)}}{\partial{\mu (k)}} = 0} & (13)\end{matrix}$

the following expression is obtained:

θ_(γ)(k+1)=θ_(γ)(k)−cot (θ_(γ))/2  (14)

Rewriting (14) as follows:

$\begin{matrix}{{\theta_{\gamma}\left( {k + 1} \right)} = {{\theta_{\gamma}(k)} - \frac{H_{n}A*{\cos \left( \theta_{\gamma} \right)}}{2*H_{n}A*{\sin \left( \theta_{\gamma} \right)}}}} & (15)\end{matrix}$

From (2), (4), and (6), it is known that

P=H _(n) A*cos (θ₁)=H _(n) A*sin (θ_(γ))  (16)

Therefore,

H _(n) A*cos (θ_(γ))=√{square root over (H _(n) ² A ² −P ²)}  (17)

Hence, substituting (16) and (17) in (15), the following is obtained:

$\begin{matrix}{{\theta_{\gamma}\left( {k + 1} \right)} = {{\theta_{\gamma}(k)} - \frac{H_{n}\sqrt{1 - \frac{{P(k)}^{2}}{A^{2}H_{n}^{2}}}}{2*{{P(k)}/A}}}} & (18)\end{matrix}$

Simplifying (18), the following position angle update equation results

$\begin{matrix}{{\theta_{\gamma}\left( {k + 1} \right)} = {{\theta_{\gamma}(k)} - \frac{\sqrt{{A^{2}H_{n}^{2}} - {P(k)}^{2}}}{2*{P(k)}}}} & (19)\end{matrix}$

Since the parameter being controlled is the position angle θ_(s) of thesolar panel, (19) can be rewritten as:

$\begin{matrix}{{\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} - \frac{\sqrt{{A^{2}H_{n}^{2}} - {P(k)}^{2}}}{2*{P(k)}}}} & (20)\end{matrix}$

The adaptive system may continue adjusting θ_(S) by the optimal amountrepresented by the ratio term in (20) at each iteration, untilθ_(S)+α=90°, and the panel is directly facing the sun and/or the maximumpower position. Since (20) contains H_(n) which is unknown, the outputpower P(k) is perturbed to a new value P(k+1) to provide a powerperturbation to P(k) and θ_(S) (k+1) is adaptively derived in each case,until the peak power H_(n)*A is achieved. Hence, (20) can be rewrittenas the following iterative relation:

$\begin{matrix}{{\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} - {\frac{\sqrt{{P\left( {k + 1} \right)}^{2} - {P(k)}^{2}}}{2*{P(k)}}.}}} & (21)\end{matrix}$

The output power P(k) is perturbed as follows,

P(k+1)=P(k)+[sgn(P(k)−P(k−1))]*(P(k)−P(k−1))  (22)

where, sgn is the signum function which as known in mathematics extractsthe sign of a real number. Thus the step size for an increment ordecrement of θ_(S) (k+1) is non-constant and generally changes with eachstep according to the above equations based on P(k) and P(k+1).

Depending on the season and time of the year, an analogous IAC algorithmbased on the description above may be followed for adjusting θ_(AZ).However, as noted above, since θ_(AZ) generally changes much moregradually as compared to θ_(S), the adjustment to θ_(AZ) needed issubstantially less frequently (e.g., weekly or monthly) as compared toθ_(S).

EXAMPLES

Disclosed embodiments of the invention are further illustrated by thefollowing specific Examples, which should not be construed as limitingthe scope or content of this Disclosure in any way.

Simulation results were generated using MATLAB software by varying thesolar elevation angle from 0 to 180 degrees (sunrise to sunset), andcalculating the tracking error and speed of convergence in each case,after a disclosed IAC algorithm converges. For each elevation angle α,the corresponding direct beam intensity H_(n) in (4) can be calculatedaccording to the equation given by

H _(n) =B*exp[−C/sin (α)]  (23)

where, B and C are site and climate related constants.

Initially, the PV panel was modeled as facing east during sunrise, i.e.α=0, and position angle θ_(S)=90°. As the day progressed, and αincreased from 0 to 180°, the IAC algorithm automatically updated θ_(S)in optimal steps to align the sun's direct rays with the normal to thePV panel. Thus, the adaptive system was able to track changes in α, andcorrespondingly H_(n), without any prior knowledge or measurement ofthese parameters. In this manner, peak power H_(n)*A was produced by thepanel throughput the day.

The resulting tracking error vs. the solar elevation angle is plotted inFIG. 4. The corresponding output power achieved by the IAC algorithm, incomparison to a PV panel fixed at 30° position angle is shown in FIG. 5.The convergence speed, in terms of number of iterations required toconverge in each case, is illustrated in FIG. 6.

From the simulation results, it is evident that the IAC algorithm yieldsexcellent tracking accuracy of the order of 10⁻³, with a significantincrease in power output as compared to a fixed PV panel. Furthermore,the IAC algorithm consistently converges in less than 50 iterations,demonstrating fast adaptation to the changes in the elevation angle ofthe sun.

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement which is calculated to achieve the same purpose maybe substituted for the specific embodiments shown. This application isintended to cover adaptations or variations of the present subjectmatter. It is to be understood that the above description is intended tobe illustrative, and not restrictive. Combinations of the aboveembodiments, and other embodiments will be apparent to those of skill inthe art upon reviewing the above description. The scope of the presentsubject matter should be determined with reference to the appendedclaims, along with the full scope of equivalents to which such claimsare entitled.

The examples that are described in the above description providesufficient detail to enable those skilled in the art to practice thesubject matter, and serve to illustrate how the subject matter may beapplied to various purposes or embodiments. References to “an”, “a”,“one”, or “some” embodiments in this disclosure are not necessarily tothe same embodiment, and such references may contemplate more than oneembodiment. Other embodiments may be utilized, and structural, logical,and electrical changes may be made without departing from the scope ofthe present disclosure.

This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Unless otherwise defined, all terms(including technical and scientific terms) used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich embodiments belongs. It will be further understood that terms,such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and will not be interpreted in anidealized or overly formal sense unless expressly so defined herein.

We claim:
 1. A photovoltaic (PV) system, comprising: at least one PV panel having a panel surface for receiving solar radiation; a power sensor coupled to an output of said PV panel for sensing an output power (P) provided by said PV panel; a system controller including at least one computing device having associated memory that provides motor control signals and implements an iterative adaptive control (IAC) algorithm stored in said memory for adjusting an angle of said PV panel; wherein said IAC algorithm includes an iterative relation that relates said P at a current time k (P(k)), its elevation angle at said k (θ_(s) (k)), said P after a next step (P(k+1)) and its elevation angle at k+1 (θ_(s) (k+1)), said IAC algorithm operable to generate a perturbed power value P(k+1) to provide a power perturbation to said P(k), and calculate a position angle θ_(S) (k+1) of said PV panel using said perturbed power value in said iterative relation; said system further comprising a mechanical system comprising a motor coupled to receive said motor control signals, said motor control signals positioning said PV panel to achieve said θ_(S) (k+1).
 2. The system of claim 1, wherein said power sensor senses said P with said PV panel positioned at said θ_(S) (k+1), said computing device: comparing a change in said P resulting from said positioning to a predetermined P change limit, and repeating said generating, calculating and said positioning provided said change in said P is ≧said predetermined P change limit.
 3. The system of claim 1, wherein said iterative relation comprises: ${\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} - {\frac{\sqrt{{P\left( {k + 1} \right)}^{2} - {P(k)}^{2}}}{2*{P(k)}}.}}$
 4. The system of claim 1, wherein said P(k+1) is generated by the following equation: P(k+1)=P(k)+[sgn(P(k)−P(k−1))]*(P(k)−P(k−1)) wherein said sgn is the signum function.
 5. The system of claim 1, wherein said system controller further implements an iterative adaptive control algorithm for iteratively adjusting an azimuthal angle θ_(AZ) of said PV panel.
 6. The system of claim 1, wherein said system is exclusive of sensors for sensing said solar radiation.
 7. A method for automatic solar tracking for a photovoltaic (PV) system comprising at least one PV panel having a panel surface for receiving solar radiation, comprising: providing an iterative adaptive control (IAC) algorithm that includes an iterative relation that relates said P at a current time k (P(k)), its elevation angle at k (θ_(s) (k)), said P after a next step (P(k+1)) and its elevation angle at k+1 (θ_(s) (k+1)); generating, by at least one computing device, a perturbed power value P(k+1) to provide a power perturbation to said P(k), calculating, by said computing device, a position angle θ_(S) (k+1) using said value for said perturbed power value in said iterative relation, and positioning said PV panel to achieve said θ_(S) (k+1).
 8. The method of claim 7, further comprising: sensing said P with said PV panel oriented at said θ_(S) (k+1); comparing a change in said P resulting from said positioning to a predetermined P change limit, and repeating said generating, calculating and said positioning provided said change in said P is ≧said predetermined P change limit.
 9. The method of claim 7, wherein said iterative relation comprises: ${\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} - {\frac{\sqrt{{P\left( {k + 1} \right)}^{2} - {P(k)}^{2}}}{2*{P(k)}}.}}$
 10. The method of claim 7, wherein said P(k+1) is generated by the following equation: P(k+1)=P(k)+[sgn(P(k)−P(k−1))]*(P(k)−P(k−1)) wherein said sgn is the signum function.
 11. The method of claim 7, further comprising implementing an iterative adaptive control algorithm for iteratively adjusting an azimuthal angle θ_(AZ) of said PV panel.
 12. The method of claim 7, wherein said method is exclusive of photosensors for sensing said solar radiation received by said PV panel.
 13. A system controller for controlling a position of at least one photovoltaic (PV) panel having a panel surface for receiving solar radiation in a PV system including a power sensor for sensing an output power (P) provided by said PV panel, and a motor for positioning said PV panel, said system controller comprising: at least one computing device having associated memory that provides motor control signals and implements an iterative adaptive control (IAC) algorithm stored in said memory for adjusting an angle of said PV panel; wherein said IAC algorithm includes an iterative relation that relates said P at a current time k (P(k)), its elevation angle at said k (θ_(s) (k)), said P after a next step (P(k+1)) and its elevation angle at k+1 (θ_(s) (k+1)), said IAC algorithm operable to generate a perturbed power value P(k+1) to provide a power perturbation to said P(k), and calculate a position angle θ_(S) (k+1) of said PV panel using said perturbed power value in said iterative relation; wherein said motor control signals from said computing device are operable to cause said motor to position said PV panel to achieve said θ_(S) (k+1).
 14. The system controller of claim 13, wherein said power sensor senses said P with said PV panel positioned at said θ_(S) (k+1), said computing device: comparing a change in said P resulting from said positioning to a predetermined P change limit, and repeating said generating, calculating and said positioning provided said change in said P is ≧said predetermined P change limit.
 15. The system controller of claim 13, wherein said iterative relation comprises: ${\theta_{S}\left( {k + 1} \right)} = {{\theta_{S}(k)} - {\frac{\sqrt{{P\left( {k + 1} \right)}^{2} - {P(k)}^{2}}}{2*{P(k)}}.}}$
 16. The system controller of claim 13, wherein said P(k+1) is generated by the following equation: P(k+1)=P(k)+[sgn(P(k)−P(k−1))]*(P(k)−P(k−1)) wherein said sgn is the signum function.
 17. The system controller of claim 13, wherein said system controller further implements an iterative adaptive control algorithm for iteratively adjusting an azimuthal angle θ_(AZ) of said PV panel.
 18. The system controller of claim 13, wherein said computing device comprises a microprocessor. 